from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				None





def 			lowercase(	)		->	Node | None:







 '''simple docstring'''
 UpperCamelCase			  =      Node(1       )
 UpperCamelCase			  =      Node(2       )
 UpperCamelCase			  =      Node(3       )
 UpperCamelCase			  =      Node(4       )
 UpperCamelCase			  =      Node(5       )
 return tree





def 			lowercase(	UpperCamelCase_       )		->	list[int]:







 '''simple docstring'''
 return [root.data, *preorder(root.left       ), *preorder(root.right       )] if root else []





def 			lowercase(	UpperCamelCase_       )		->	list[int]:







 '''simple docstring'''
 return postorder(root.left       ) + postorder(root.right       ) + [root.data] if root else []





def 			lowercase(	UpperCamelCase_       )		->	list[int]:







 '''simple docstring'''
 return [*inorder(root.left       ), root.data, *inorder(root.right       )] if root else []





def 			lowercase(	UpperCamelCase_       )		->	int:







 '''simple docstring'''
 return (max(height(root.left       )							,					height(root.right       )       ) + 1) if root else 0





def 			lowercase(	UpperCamelCase_       )		->	Sequence[Node | None]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 if root is None:
  return output

 UpperCamelCase			  =      deque([root]       )

 while process_queue:
  UpperCamelCase			  =      process_queue.popleft()
  output.append(node.data       )

  if node.left:
   process_queue.append(node.left       )
  if node.right:
   process_queue.append(node.right       )
 return output





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Sequence[Node | None]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 def populate_output(UpperCamelCase_							,					UpperCamelCase_       ) -> None:
  if not root:
   return
  if level == 1:
   output.append(root.data       )
  elif level > 1:
   populate_output(root.left							,					level - 1       )
   populate_output(root.right							,					level - 1       )

 populate_output(UpperCamelCase_							,					UpperCamelCase_       )
 return output





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Sequence[Node | None]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 def populate_output(UpperCamelCase_							,					UpperCamelCase_       ) -> None:
  if root is None:
   return
  if level == 1:
   output.append(root.data       )
  elif level > 1:
   populate_output(root.right							,					level - 1       )
   populate_output(root.left							,					level - 1       )

 populate_output(UpperCamelCase_							,					UpperCamelCase_       )
 return output





def 			lowercase(	UpperCamelCase_       )		->	Sequence[Node | None] | list[Any]:







 '''simple docstring'''
 if root is None:
  return []

 UpperCamelCase			  =      []

 UpperCamelCase			  =      0
 UpperCamelCase			  =      height(UpperCamelCase_       )

 for h in range(1							,					height_tree + 1       ):
  if not flag:
   output.append(get_nodes_from_left_to_right(UpperCamelCase_							,					UpperCamelCase_       )       )
   UpperCamelCase			  =      1
  else:
   output.append(get_nodes_from_right_to_left(UpperCamelCase_							,					UpperCamelCase_       )       )
   UpperCamelCase			  =      0

 return output





def 			lowercase(	)		->	None:  # Main function for testing.







 '''simple docstring'''
 UpperCamelCase			  =      make_tree()
 print(f"""In-order Traversal: {inorder(UpperCamelCase_       )}"""       )
 print(f"""Pre-order Traversal: {preorder(UpperCamelCase_       )}"""       )
 print(f"""Post-order Traversal: {postorder(UpperCamelCase_       )}"""							,					"""\n"""       )

 print(f"""Height of Tree: {height(UpperCamelCase_       )}"""							,					"""\n"""       )

 print("""Complete Level Order Traversal: """       )
 print(level_order(UpperCamelCase_       )							,					"""\n"""       )

 print("""Level-wise order Traversal: """       )

 for level in range(1							,					height(UpperCamelCase_       ) + 1       ):
  print(f"""Level {level}:"""							,					get_nodes_from_left_to_right(UpperCamelCase_							,					level=UpperCamelCase_       )       )

 print("""\nZigZag order Traversal: """       )
 print(zigzag(UpperCamelCase_       )       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE 		=      logging.getLogger()

_SCREAMING_SNAKE_CASE 		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    os.makedirs(lowerCamelCase_   ,   exist_ok=lowerCamelCase_				)
    UpperCamelCase			  =      {"""source""": """What is love ?""", """target""": """life"""}
    UpperCamelCase			  =      {"""train""": 12, """val""": 2, """test""": 2}
    for split in ["train", "test", "val"]:
     for field in ["source", "target"]:
      UpperCamelCase			  =      """\n""".join([contents[field]] * n_lines[split]				)
      with open(os.path.join(lowerCamelCase_   ,   f"""{split}.{field}"""				)   ,   """w"""				) as f:
       f.write(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str = "pytorch"				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_auto_remove_tmp_dir()
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """output"""				)
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """data"""				)
    self._create_dummy_data(data_dir=lowerCamelCase_				)

    UpperCamelCase			  =      f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

    if gpus > 0:
     testargs.append(f"""--gpus={gpus}"""				)
     if is_apex_available():
      testargs.append("""--fp16"""				)
    else:
     testargs.append("""--gpus=0"""				)
     testargs.append("""--distributed_backend=ddp_cpu"""				)
     testargs.append("""--num_processes=2"""				)

    UpperCamelCase			  =      [sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
    execute_subprocess_async(lowerCamelCase_   ,   env=self.get_env()				)

    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """metrics.json"""				)
    with open(lowerCamelCase_				) as f:
     UpperCamelCase			  =      json.load(lowerCamelCase_				)
    return result







   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=2				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)
